Productivity measurement is particularly useful for individuals who lead enterprise governance and measurement programs, in addition to practitioners working on business-critical software. Pragmatic approaches to automated software measurement are more important than ever, especially as the shift to digital continues.

As CAST’s Head of Product Development recently wrote in Computer Weekly, the main struggle for IT-intensive organizations as they push toward transformation are the layers of software complexity that have been amassed over years of doing business. Even as new methodologies, like microservices, DevOps and Agile, are adopted to streamline development, instituting automated and standards-based productivity measures is key to success.

Software productivity measurement metrics are essential to ensure development teams provide the best value in the shortest amount of time while helping their organizations determine the amount of required input to complete a software project. Standards-based measures, like those supported by CISQ, are essential to define a performance baseline in addition to productivity goals the organization wants to achieve. An important first step in software productivity measurement is to establish a performance baseline and determine improvement goals.

Once goals are set, the next step is to determine how teams will deliver functionality using normalized measures. Automated Function Points are a good unit of measure because they are objective, repeatable and can be performed on any application whether it is new or existing. Another important step is to define at which moment measurement should be performed depending on the development methods used.

This is where it gets particularly important to use normalized measures in determining functionality delivered by dev teams. Normalized measures will apply regardless of the number of code lines or type of development work completed. As a result, organizations can accurately quantify how much output is provided based on the number of completed business functions.

In order to optimize software productivity measurement, it should also account for the organization’s development processes and environment: the number of programming languages, complexity of the application and the type of applications developed.

Whether you want to measure the productivity of your development teams, measure the structural quality of your business-critical systems, or prevent software risk from leading to major outages or data corruption, a software analysis and measurement system is no longer an option.